Data Engineer - AI & Analytics

Job Summary

• Work with limited supervision to support data engineering and data analytics solutions. Provide design guidance and implementation for end-to-end analytic solutions. Coordinate with SWA focused groups to create unique data infrastructure, run tests on their designs to isolate errors and update systems to accommodate changes in company needs

Responsibilities

• Assemble large, complex sets of data that meet non-functional and functional business requirements
• Identify, design and implement internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes
• Build required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS and SQL technologies
• Build analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition
• Work with stakeholders including data, design, product and executive teams and assisting them with data-related technical issues
• Work with stakeholders including the Executive, Product, Data and Design teams to support their data infrastructure needs while assisting with data-related technical issues
• Generate or adapt equipment and technology to serve user needs
• May perform other job duties as directed by Employee's Leaders
• Advanced SQL development, data modelling, performance tuning, and enterprise-scale analytical datasets.
• Advanced Python development for reusable data frameworks, automation, testing, and production-grade engineering
• Design and ownership of scalable ETL/ELT architectures, data lake, lakehouse, and enterprise integration patterns.
• Advanced AWS data engineering experience across Glue, S3, Athena, Redshift, EMR, Kinesis, Step Functions, Airflow/MWAA, security, and reliability.
• Data quality, governance, APIs, CI/CD pipelines, observability, automated testing, and production support leadership
• Lead scalable enterprise data engineering foundations supporting Agent Observability, Ops Enablement, and future autonomous operations.
• Own telemetry ingestion, data processing pipelines, evaluation datasets, and reusable data engineering frameworks on AWS.
• Serve as the technical partner across Product, AI, Operations, and Engineering teams while improving platform reliability, governance, and scalability.

Knowledge, Skills and Abilities

• Knowledge of the practical application of engineering science and technology, including applying principles, techniques, procedures, and equipment to the design and production of various goods and services
• Knowledge of design techniques, tools, and principles involved in production of precision technical plans, blueprints, drawings, and models
• Ability to use logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions or approaches to problems
• Ability to understand new information for both current and future problem-solving and decision-making
• Skilled in identifying complex problems and reviewing related information to develop and evaluate options and implement solutions
• Ability to recognize when an issue has occurred or is likely to occur, without needing to diagnose or resolve the problem.
• Ability to combine pieces of information to form general rules or conclusions (includes finding a relationship among seemingly unrelated events)
• Ability to switch efficiently between multiple tasks or information sources, such as spoken instructions, system alerts, or data inputs.
• Ability to organize data or actions in a defined sequence or structure based on specified rules or patterns (e.g., numerical, textual, visual, or mathematical sequences).
• Ability to recognize defined patterns—such as shapes, words, or signals—even when they are embedded within distracting or complex information.
• Advanced SQL development, data modelling, performance tuning, and enterprise-scale analytical datasets.
• Advanced Python development for reusable data frameworks, automation, testing, and production-grade engineering
• Design and ownership of scalable ETL/ELT architectures, data lake, lakehouse, and enterprise integration patterns.
• Advanced AWS data engineering experience across Glue, S3, Athena, Redshift, EMR, Kinesis, Step Functions, Airflow/MWAA, security, and reliability.
• Data quality, governance, APIs, CI/CD pipelines, observability, automated testing, and production support leadership
• Databricks and Apache Spark
• Iceberg, Delta Lake, and Hudi
• Kafka, Kinesis, and streaming architectures
• AI/GenAI telemetry, vector databases, RAG analytics, QuickSight, or Grafana

Education

• Required: Bachelor's degree in Computer Science, Engineering, Information Systems or related field and/or equivalent formal training

Experience

• Required: Intermediate level experience, fully functioning broad knowledge in:
  • Cloud infrastructure, DataLake
  • ETL experience ensuring source to target data integrity
  • Various filetypes (Delimited Text, Fixed Width, XML, JSON, Parque).
  • ServiceBus, setting up ingress and egress within a subscription, or relevant AWS Cloud services administrative experience.
  • Unit Testing, Code Quality tools, CI/CD Technologies, Security and Container Technologies
  • Agile development experience and Agile ceremonies and practices
  • 2-5 years of relevant work-related experience
  • Advanced SQL development, data modelling, performance tuning, and enterprise-scale analytical datasets.
  • Advanced Python development for reusable data frameworks, automation, testing, and production-grade engineering
  • Design and ownership of scalable ETL/ELT architectures, data lake, lakehouse, and enterprise integration patterns.
  • Advanced AWS data engineering experience across Glue, S3, Athena, Redshift, EMR, Kinesis, Step Functions, Airflow/MWAA, security, and reliability.
  • Data quality, governance, APIs, CI/CD pipelines, observability, automated testing, and production support leadership

Preferred Experience

• Databricks and Apache Spark
• Iceberg, Delta Lake, and Hudi
• Kafka, Kinesis, and streaming architectures
• AI/GenAI telemetry, vector databases, RAG analytics, QuickSight, or Grafana

Other Qualifications

• Must meet confidentiality expectations as to confidential, proprietary and sensitive Company information
• Ability to work extended hours as needed
• Ability to work onsite approximately 3 days per week and as required by the business.